Page 76 - Read Online

Pandey et al. J Transl Genet Genom 2021;5:22-36 I http://dx.doi.org/10.20517/jtgg.2020.45 Page 27
th
the value of the corresponding variables/parameters for the i individual. The j subscript denotes the
th
corresponding variables/parameters at j time. The residual error shown above is an additive term in the
model output. Other types of residual error models are proportional, exponential, combined additive and
[53]
proportional, and combined additive and exponential functions of ε . The IIV is described in Equation
(2) using an exponential function. The other functions used to describe IIV are additive and proportional.
Population PK studies have been done in cancer and SCA patients.

Pharmacokinetic studies of HU in HIV described the HU plasma concentration–time data with a
one- or two-compartment model with first-order absorption and first-order elimination [20,47] . One
study demonstrated a significant correlation between predicted and observed serum concentrations of
[19]
[20]
hydroxyurea . Tracewell et al. studied population PK of HU in cancer patients. A one-compartment
model fitted the patients’ data with elimination through the metabolic and renal pathways. Michaelis-
Menten kinetics was used for metabolic elimination, and a first-order rate equation was used for renal
elimination . The IIV for the volume of distribution, V, was assumed to be proportional to the average
[19]
[19]
value through the following equation :

(3)
_
th
where V is i individual V, V is the average V of population, and η is the random variable that denotes
Vi
i
[19]
IIV. To account for RV, the residual error model was described by the proportional function given below :
(4)

th
th
where y is the model-predicted drug concentration of i individual at j time.
Mij
The PK-PD studies in cancer and HIV patients found one- or two-compartment models with first-
order absorption and first-order or Michaelis-Menten elimination to best fit the drug concentration-
[32]
time profile [19,20,47] . In the case of SCD, Ware et al. studied the PK after the first dose of HU using non-
compartmentalized PK analysis. They observed two categories of patients with varying absorption profiles,
slow and fast, and with varying drug exposure. The apparent clearance, CL/F, depends on the weight of the
patient, as determined from the least-squares regression fit. Univariate and multivariate linear regression
was done to identify significant covariates for CL/F. The coefficient of variation in PK parameters described
the IIV. In multivariate analysis, covariates related to CL/F were weight, alanine aminotransferase (ALT),
[32]
and serum creatinine .

[33]
The population PK-PD model in SCA patients developed by Paule et al. captured the relationship
between exposure-efficacy and corresponding variability in PK-PD. The second-order conditional
estimation method was used to obtain the PK parameter estimates with the interaction between inter-
individual and residual variabilities. The two-compartment model with first-order absorption and first-
order elimination fitted the PK data best. The combined additive and proportional residual error model
described the RV, as shown below :
[33]

(5)

where ε and ε are the proportional and additive residual random errors, respectively. A scaling factor for
aij
pij
the central volume of distribution, V, and clearance, CL, was used to scale these parameters by body weight
c
(BW) of a 70-kg patient. The scaling was done to adapt the model to children, as given by the following
[33]
equation :
(6)